DATA 5620/6620 Advanced Regression for Causal Inference
This course focuses on the application of regression to inform decision-making, particularly using interpretable models to understand the effect of interventions on business outcomes. Students learn to model experimental and observational data and infer causality instead of correlation only. Prerequisite: DATA 5600
By the end of this course, you will be able to:
Success in this course is demonstrating conceptual understanding and skill mastery by applying the modeling workflow in your chosen business context and as part of a group
You are each an essential member of our community of learners; please consider me a teacher and a mentor
Assignments are designed to be aligned with what you will be expected to do in practice
| A | 93-100% | B- | 80-82% | D+ | 67-69% |
| A- | 90-92% | C+ | 77-79% | D | 63-66% |
| B+ | 87-89% | C | 73-76% | D- | 60-62% |
| B | 83-86% | C- | 70-72% | E | 0-59% |
This class is all about participation: If you aren’t attending, you can’t contribute
Interviews are an opportunity for you to demonstrate your personal understanding and prepare for future real-world job interviews
Projects are the focus of learning by doing in the course, serving as the means for you to apply your conceptual understanding and skill mastery both as a group and within your business domain of interest
Supervised learning
Unsupervised learning
Reinforcement learning
\[ \Huge{f: X \rightarrow Y} \]
\(X\)
\(Y\)
We use models to extract information from data and inform decisions in the presence of uncertainty
We use models to extract information from data and inform decisions in the presence of uncertainty
We use models to extract information from data and inform decisions in the presence of uncertainty
We use models to extract information from data and inform decisions in the presence of uncertainty
We use models to extract information from data and inform decisions in the presence of uncertainty